Modeling Urban Land Use Changes Using Support Vector Machines

Support Vector Machines (SVM) is a machine learning (ML) algorithm commonly applied to the classification of remotely sensing data and more recently for modeling land use changes. However, in most geospatial applications the current literature does not elaborate on specifications of the SVM method with respect to data sampling, attribute selection and optimal parameters choices. Therefore the main objective of this study is to present and investigate the SVM technique for modeling urban land use change. The SVM model building procedure is presented together with the detailed evaluation of the output results with respect to the choice of datasets, attributes and the change of SVM parameters. Geospatial datasets containing nine land use classes and spatial attributes for the Municipality of Zemun, Republic of Serbia were used for years 2001, 2003, 2007 and 2011. The Correlation‐based Feature Subset method, kappa coefficient, Area Under Receiver Operating Characteristic Curve (AUC) and kappa simulation were used to perform the model evaluation and compare the model outputs with the real land use datasets. The obtained results indicate that the SVM‐based models perform better when implementing balanced data sampling, reduced data sets to informative subsets of attributes and properly identify the optimal learning parameters.     

Enhancing a GIS Cellular Automata Model of Land Use Change: Bayesian Networks, Influence Diagrams and Causality

Cellular Automata (CA) models at present do not adequately take into account the relationship and interactions between variables. However, land use change is influenced by multiple variables and their relationships. The objective of this study is to develop a novel CA model within a geographic information system (GIS) that consists of Bayesian Network (BN) and Influence Diagram (ID) sub‐models. Further, the proposed model is intended to simplify the definition of parameter values, transition rules and model structure. Multiple GIS layers provide inputs and the CA defines the transition rules by running the two sub‐models. In the BN sub‐model, land use drivers are encoded with conditional probabilities extracted from historical data to represent inter‐dependencies between the drivers. Using the ID sub‐model, the decision of changing from one land use state to another is made based on utility theory. The model was applied to simulate future land use changes in the Greater Vancouver Regional District (GVRD), Canada from 2001 to 2031. The results indicate that the model is able to detect spatio‐temporal drivers and generate various scenarios of land use change making it a useful tool for exploring complex planning scenarios.     

Knowledge Transfer for Large‐Scale Urban Growth Modeling Based on Formal Concept Analysis

Cellular automata (CA) are useful for studies on urban growth and land‐use changes. Although various methods have been developed to define transition rules, modeling urban growth of large areas remains a tough challenge owing to heterogeneous geographical features. To address the problem, we present a novel method based on the combination of Formal Concept Analysis (FCA) and knowledge transfer techniques. FCA is used to solicit association rules among cities within a large area. This method can provide a theoretical basis for the knowledge transfer process. A cutting‐edge algorithm called TrAdaBoost is then integrated with the commonly‐used Logistic‐CA as the modeling framework. The proposed method is applied to the urban growth modeling of Guangdong Province, a large region with 21 cities in China, from 2005 to 2008. Compared with traditional methods, this method can achieve better results at the provincial and local levels, according to the experiments. The combination of FCA and knowledge transfer is expected to provide a useful tool for calibrating large‐scale urban CA models.     

A Patch‐based Cellular Automaton for Simulating Land‐use Changes at Fine Spatial Resolution

While cellular automata have become popular tools for modeling land‐use changes, there is a lack of studies reporting their application at very fine spatial resolutions (e.g. 5 m resolution). Traditional cell‐based CA do not generate reliable results at such resolutions because single cells might only represent components of land‐use entities (i.e. houses or parks in urban residential areas), while recently proposed entity‐based CA models usually ignore the internal heterogeneity of the entities. This article describes a patch‐based CA model designed to deal with this problem by integrating cell and object concepts. A patch is defined as a collection of adjacent cells that might have different attributes, but that represent a single land‐use entity. In this model, a transition probability map was calculated at each cell location for each land‐use transition using a weight of evidence method; then, land‐use changes were simulated by employing a patch‐based procedure based on the probability maps. This CA model, along with a traditional cell‐based model were tested in the eastern part of the Elbow River watershed in southern Alberta, Canada, an area that is under considerable pressure for land development due to its proximity to the fast growing city of Calgary. The simulation results for the two models were compared to historical data using visual comparison, K_simulation indices, and landscape metrics. The results reveal that the patch‐based CA model generates more compact and realistic land‐use patterns than the traditional cell‐based CA. The K_simulation values indicate that the land‐use maps obtained with the patch‐based CA are in higher agreement with the historical data than those created by the cell‐based model, particularly regarding the location of change. The landscape metrics reveal that the patch‐based model is able to adequately capture the land‐use dynamics as observed in the historical data, while the cell‐based CA is not able to provide a similar interpretation. The patch‐based approach proposed in this study appears to be a simple and valuable solution to take into account the internal heterogeneity of land‐use classes at fine spatial resolutions and simulate their transitions over time.     

基于时空自相关的建筑物变化检测

针对传统基于像素级的变化检测方法在变化分析中难以利用像元间的时空关系、变化检测结果精度低的问题,提出了一种基于时空自相关的建筑物变化检测方法。首先,利用形态学建筑指数(Morphological Building Index,MBI)进行建筑物提取,并通过长宽比、面积等剔除道路信息优化建筑物提取;其次,采用时空自相关模型分别构建两期MBI特征影像的时空自相关性指标值作为对应像元的相似性测度;最后,利用最大类间方差(otsu)法确定最优阈值,得到变化检测结果。实验表明,该方法所得变化检测结果更完整,漏检率和误检率均低于对比算法,该方法基本满足变化检测需求,为高分影像建筑物变化检测提供一种新的技术手段。     

基于MODIS 250m遥感影像的变化检测

本文采用分辨率为250m的MODIS遥感影像对土地利用类型进行动态监测,其目的是探讨在MODIS影像上,能够快速准确地获取在不同时期的变化信息,并且为进一步分析提供参考。本文主要讨论运用MODIS影像进行变化检测的几个方面:一是相对辐射校正;二是变化区域的确定,采用基于像素的阈值分割和基于区域的松弛法进来确认变化/非变化像元,并提出一种基于缓冲区方法来消除部分伪变化信息;三是精度评价,根据MO-DIS高时相的特点,本文提出了基于缺少实地观测数据情况下的精度评价。     

基于EM和BIC的直方图拟合方法应用于遥感变化检测阈值确定

利用遥感图像进行变化检测时,确定"差异图像"上各变化类型的阈值非常关键.本文引入图像直方图拟合方法来确定变化阈值.首先通过基于变化向量分析方法,得到变化强度图像,然后假设该变化强度图像中的像元值符合混合高斯分布模型,利用期望最大(EM)算法和贝叶斯信息准则(BIC)求出最佳的混合高斯分布模型,拟合此时的图像直方图,最后利用贝叶斯判别准则确定出各变化类型的变化阈值.试验证明,这种方法是一种较为有效的自动确定变化阈值的方法.     

A multi‐label cellular automata model for land change simulation

The use of cellular automata (CA) has for some time been considered among the most appropriate approaches for modeling land‐use changes. Each cell in a traditional CA model has a state that evolves according to transition rules, taking into consideration its own and its neighbors’ states and characteristics. Here, we present a multi‐label CA model in which a cell may simultaneously have more than one state. The model uses a multi‐label learning method—a multi‐label support vector machine, Rank‐SVM—to define the transition rules. The model was used with a multi‐label land‐use dataset for Luxembourg, built from vector‐based land‐use data using a method presented here. The proposed multi‐label CA model showed promising performance in terms of its ability to capture and model the details and complexities of changes in land‐use patterns. Applied to historical land use data, the proposed model estimated the land use change with an accuracy of 87.2% exact matching and 98.84% when including cells with a misclassification of a single label, which is comparably better than a classical multi‐class model that achieved 83.6%. The multi‐label cellular automata outperformed a model combining CA and artificial neural networks. All model goodness‐of‐fit comparisons were quantified using various performance metrics for predictive models.     

改进的CR-InSAR技术用于四川甲居滑坡形变监测

采用一种改进的基于相位相干性的非线性人工角反射器InSAR解算算法,不仅能有效地避免相位解缠误差,而且对于大尺度形变梯度具有较强的探测能力。将该方法用于四川丹巴县甲居滑坡形变监测试验,获取了位于甲居滑坡上角反射器的形变结果。通过与小基线集InSAR算法以及基于解缠相位的CR-InSAR算法结果比较,该方法更能准确获取非线性形变时间序列结果,合理地揭示了甲居滑坡明显的形变特征。     

时间序列空间数据可视化中有关问题的研究

就土地利用变化可视化这一实例 ,对时间序列空间数据可视化的图形关系分析和图形内插等问题进行了深入的研究 ,得出了土地利用变化中图形关系变化的四种基本形式 ,并针对这四种基本图形的变化情形研究出了相应的内插策略 ;在对现有的图形内插算法研究的基础上 ,提出了基于物理场模型的整体内插算法 ,该算法能够较好地解决文中的图形内插问题。     

Revealing Uncertainties in Land Change Modeling Using Probabilities

Land change models are frequently used to analyze current land change processes and possible future developments. However, the outcome of such models is accompanied by uncertainties that have to be taken into account in order to address their reliability for science and decision‐making. While a range of approaches exist that quantify the disagreement of land change maps, the quantification of uncertainty remains a major challenge. The aim of this article is therefore to reveal uncertainties in land change modeling by developing two measures: quantity uncertainty and allocation uncertainty. We choose a Bayesian Belief Network modeling approach for deforestation in Brazil to develop and apply the two measures to the resulting probability surface. Quantity uncertainty describes the uncertainty about the correct number of cells in a land change map assigned to different land change categories and allocation uncertainty expresses the uncertainty about the correct spatial placement of a cell in the land change map. Thus, uncertainty can be quantified even in those cases where no reference data exist. Informing about uncertainty in probabilistic outcomes may be an important asset when land change projections are being used in science and decision‐making and moreover, they may also be further evaluated for other spatial applications.     

结合IR-MAD与均值漂移算法的密集城区遥感影像变化检测

不同时相遥感影像变化检测已成为土地利用变更调查、城市扩张分析、自然灾害分析及其他环境问题必不可少的技术手段之一。本文提出了一种结合IR-MAD与均值漂移算法的密集城区遥感影像变化检测方法。该方法通过伪不变特征法完成两期影像的相对辐射校正,有效改善影像间的配准误差,并利用IR-MAD算法对校正后的影像进行迭代运算,采用均值漂移算法对迭代后的影像进行分割,同时运用形态学方法处理分割后的影像,最终提取变化图斑。试验结果表明,该方法可以有效检测出变化区域,可应用于城市地表覆盖的变化检测。     

A Temporal Variant‐Invariant Validation Approach for Agent‐based Models of Landscape Dynamics

Agent‐based modeling provides a means for addressing the way human and natural systems interact to change landscapes over time. Until recently, evaluation of simulation models has focused on map comparison techniques that evaluate the degree to which predictions match real‐world observations. However, methods that change the focus of evaluation from patterns to processes have begun to surface; that is, rather than asking if a model simulates a correct pattern, models are evaluated on their ability to simulate a process of interest. We build on an existing agent‐based modeling validation method in order to present a temporal variant‐invariant analysis (TVIA). The enhanced method, which focuses on analyzing the uncertainty in simulation results, examines the degree to which outcomes from multiple model runs match some reference to how land use parcels make the transition from one land use class to another over time. We apply TVIA to results from an agent‐based model that simulates the relationships between landowner decisions and wildfire risk in the wildland‐urban interface of the southern Willamette Valley, Oregon, USA. The TVIA approach demonstrates a novel ability to examine uncertainty across time to provide an understanding of how the model emulates the system of interest.     

谐波改进的植被指数时间序列重建算法

提出一种基于傅里叶谐波分析的改进算法,引入异常值检测算法,检测拟合过程中的异常值,增加数据拟合的真实性;迭代前动态估算出待处理序列点的峰值个数(即频数),解决整个区域预设单一频数的不合理性;引入拟合影响因子,自动控制迭代终止条件,避免传统方法中人为设置阈值导致的不确定性。利用2003年华北平原MODIS_EVI时间序列图像验证表明,较之HANTS算法,改进算法能够有效修正噪声污染像元值,修正后的EVI时序曲线更能反映地物内在的物候变化规律,并能够更好地保真原始曲线上的特征(点),如作物EVI最大值、最小值出现的时间和大小关系。     

遥感数据时空尺度对地理要素时空变化分析的影响

地理要素的时空变化分析对于了解和掌握地表的规律性有着重要的作用。利用地面站点观测、实地调查等传统方式获取数据,对地理要素进行时空变化分析是最常用的方法。但该类方法往往表现的是"点尺度"观测,不能在大尺度情况下准确地反映地表的时空变化信息。遥感卫星能以一定的时间间隔获得空间连续的对地"面尺度"观测数据,然而其特定的空间和时间分辨率使其获取的地表信息仍十分有限。同时地表的空间异质性和个别地表短时间内的快速变化,使得利用遥感数据对地理要素进行时空变化分析时,时空变化分析结果会随观测尺度而发生改变。本文从空间尺度和时间尺度两方面综述遥感数据时空尺度对地理要素的时空变化分析产生的影响和原因,并针对这些问题总结了减小时空尺度对结果不确定性影响的现行方法。可以通过多源遥感协同观测和反演、尺度转换、空间建模等方法减小空间尺度引起的不确定性;通过联合多时相遥感数据的方法减小时间尺度引起的不确定性。在实际应用中,应根据所观测地理要素的实际情况,综合分析选择合适的方法。     

基于变化检测的视频对象提取及后继帧的对象跟踪

提出一种多尺度帧间边界变化检测方法，将当前图像划分为变化区域和非变化区域，变化区域内采用基于Hausdorff距离跟踪器找到对象在后继帧的最佳匹配位置；然后利用Snake模型拟合该位置上的非刚性形变，得到对象真实边缘；最后采用一种基于距离变换的最短路径法使开环闭合。     

用遗传算法反演连续植被的组分温度

由于热红外多波段数据间具有高度的相关性和混合像元的大量存在，使得多波段陆面温度反演精度难以提高，并且难以得到组分温度信息。在连续植被热辐射方向性规律上的基础上，以喜直型连续植被为例，进行了大量的Monte-Carlo模拟，建立了组分有效比辐射率与土壤表面比辐射率和植被叶面积指数之间的经验函数关系，并以此构造目标函数，采用遗传算法，从热红外多角度数据中，同时反演混合像元组分温度和土壤比辐射率以及叶面积指数。通过对模拟的观测数据进行遗传算法反演的大量试验，结果表明，遗传算法反演组分温度非常稳健，在宽松的先验知识条件下，遗传算法可以解决不确定性反演问题。遗传算法反演结果和野外实测数据作了比较，证实了反演原理的正确性，为基于热红外方向性辐射模型反演组分温度，提供了新方法。     

土地利用变化模拟模型及应用研究进展

元胞自动机CA(Cellular Automata)和多智能体ABM(Agent-Based Model)模型是土地利用格局和演化模拟的主流方法,两者在模拟自然因素影响和人文驱动机制方面具有突出优势,为LUCC研究提供了重要的工具。当前,ABM无论在模型构建还是应用研究方面,CA和ABM均取得了显著进展。论文从数据基础、模拟尺度、CA转换规则挖掘、ABM行为规则定义、CA和ABM的耦合4个方面梳理土地利用模拟模型和方法的研究进展。并总结这些模型在虚拟城市模拟与理论验证、真实城市模拟与规划预测以及多类用地模拟与辅助决策等方面的应用。最后,总结土地利用模拟模型在精细模拟和全球变化研究方面存在的局限性,认为未来发展将主要集中于解决从2维模型向3维模型发展、大数据与规则精细挖掘以及大尺度模拟与知识迁移等问题。     

武夷山市土地利用变化遥感监测分析

针对近20年来武夷山市土地利用变化信息相对不足的问题,提出运用现代遥感与GIS技术进行武夷山市土地利用变化动态监测的方法。在分析1996年、2005年和2014年3期Landsat影像的基础上,采用监督分类方法提取土地利用信息,然后采用土地利用转移矩阵分析法和动态度分析法,分析了土地利用类型的时空变化情况及发展趋势。结果表明:1996年以来,武夷山市土地利用变化的特点表现为耕地显著增加,而林地明显减少;20年间其他未利用地和城镇的空间变化幅度很大;经济快速发展,旅游业大力发展等是武夷山市土地利用变化的驱动因素。研究结果对武夷山市的生态环境保护以及探究旅游业对土地利用变化的影响有参考价值。